





'''simple docstring'''






from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFTransfoXLForSequenceClassification,
          TFTransfoXLLMHeadModel,
          TFTransfoXLModel,
      )







class 		UpperCAmelCase :


  """simple docstring"""







  def __init__(					self     :       str				,						UpperCamelCase__     :       List[str]				,						)  ->    Optional[int]:
    _UpperCamelCase							      =parent
    _UpperCamelCase							      =13
    _UpperCamelCase							      =7
    _UpperCamelCase							      =30
    _UpperCamelCase							      =self.seq_length + self.mem_len
    _UpperCamelCase							      =15
    _UpperCamelCase							      =True
    _UpperCamelCase							      =True
    _UpperCamelCase							      =99
    _UpperCamelCase							      =[10, 50, 80]
    _UpperCamelCase							      =32
    _UpperCamelCase							      =32
    _UpperCamelCase							      =4
    _UpperCamelCase							      =8
    _UpperCamelCase							      =128
    _UpperCamelCase							      =2
    _UpperCamelCase							      =2
    _UpperCamelCase							      =None
    _UpperCamelCase							      =1
    _UpperCamelCase							      =0
    _UpperCamelCase							      =3
    _UpperCamelCase							      =self.vocab_size - 1
    _UpperCamelCase							      =0.01


  def 	UpperCamelCase__   (					self     :       Optional[Any]	)  ->    Tuple:
    _UpperCamelCase							      =ids_tensor([self.batch_size, self.seq_length]				,						self.vocab_size	)
    _UpperCamelCase							      =ids_tensor([self.batch_size, self.seq_length]				,						self.vocab_size	)

    _UpperCamelCase							      =None
    if self.use_labels:
      _UpperCamelCase							      =ids_tensor([self.batch_size, self.seq_length]				,						self.vocab_size	)

    _UpperCamelCase							      =TransfoXLConfig(
        vocab_size=self.vocab_size				,						mem_len=self.mem_len				,						clamp_len=self.clamp_len				,						cutoffs=self.cutoffs				,						d_model=self.hidden_size				,						d_embed=self.d_embed				,						n_head=self.num_attention_heads				,						d_head=self.d_head				,						d_inner=self.d_inner				,						div_val=self.div_val				,						n_layer=self.num_hidden_layers				,						eos_token_id=self.eos_token_id				,						pad_token_id=self.vocab_size - 1				,						init_range=self.init_range				,						num_labels=self.num_labels				,						)

    return (config, input_ids_a, input_ids_a, lm_labels)


  def 	UpperCamelCase__   (					self     :       Any	)  ->    Optional[int]:
    random.seed(self.seed	)
    tf.random.set_seed(self.seed	)


  def 	UpperCamelCase__   (					self     :       Union[str, Any]				,						UpperCamelCase__     :       int				,						UpperCamelCase__     :       int				,						UpperCamelCase__     :       Optional[Any]				,						UpperCamelCase__     :       int	)  ->    str:
    _UpperCamelCase							      =TFTransfoXLModel(UpperCamelCase__	)

    _UpperCamelCase      ,  _UpperCamelCase							      =model(UpperCamelCase__	).to_tuple()

    _UpperCamelCase							      ={'''input_ids''': input_ids_a, '''mems''': mems_a}

    _UpperCamelCase      ,  _UpperCamelCase							      =model(UpperCamelCase__	).to_tuple()

    self.parent.assertEqual(hidden_states_a.shape				,						(self.batch_size, self.seq_length, self.hidden_size)	)
    self.parent.assertEqual(hidden_states_a.shape				,						(self.batch_size, self.seq_length, self.hidden_size)	)
    self.parent.assertListEqual(
        [mem.shape for mem in mems_a]				,						[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers				,						)
    self.parent.assertListEqual(
        [mem.shape for mem in mems_a]				,						[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers				,						)


  def 	UpperCamelCase__   (					self     :       Tuple				,						UpperCamelCase__     :       str				,						UpperCamelCase__     :       Optional[int]				,						UpperCamelCase__     :       List[Any]				,						UpperCamelCase__     :       Optional[Any]	)  ->    Dict:
    _UpperCamelCase							      =TFTransfoXLLMHeadModel(UpperCamelCase__	)

    _UpperCamelCase      ,  _UpperCamelCase							      =model(UpperCamelCase__	).to_tuple()

    _UpperCamelCase							      ={'''input_ids''': input_ids_a, '''labels''': lm_labels}
    _UpperCamelCase      ,  _UpperCamelCase							      =model(UpperCamelCase__	).to_tuple()

    _UpperCamelCase      ,  _UpperCamelCase							      =model([input_ids_a, mems_a]	).to_tuple()

    _UpperCamelCase							      ={'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

    _UpperCamelCase      ,  _UpperCamelCase							      =model(UpperCamelCase__	).to_tuple()

    self.parent.assertEqual(lm_logits_a.shape				,						(self.batch_size, self.seq_length, self.vocab_size)	)
    self.parent.assertListEqual(
        [mem.shape for mem in mems_a]				,						[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers				,						)

    self.parent.assertEqual(lm_logits_a.shape				,						(self.batch_size, self.seq_length, self.vocab_size)	)
    self.parent.assertListEqual(
        [mem.shape for mem in mems_a]				,						[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers				,						)


  def 	UpperCamelCase__   (					self     :       Tuple				,						UpperCamelCase__     :       Any				,						UpperCamelCase__     :       int				,						UpperCamelCase__     :       Tuple				,						UpperCamelCase__     :       Optional[Any]	)  ->    str:
    _UpperCamelCase							      =TFTransfoXLForSequenceClassification(UpperCamelCase__	)
    _UpperCamelCase							      =model(UpperCamelCase__	)
    self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.num_labels)	)




  def 	UpperCamelCase__   (					self     :       str	)  ->    str:
    _UpperCamelCase							      =self.prepare_config_and_inputs()
    ((_UpperCamelCase)      ,  (_UpperCamelCase)      ,  (_UpperCamelCase)      ,  (_UpperCamelCase))							      =config_and_inputs
    _UpperCamelCase							      ={'''input_ids''': input_ids_a}
    return config, inputs_dict







@require_tf
class 		UpperCAmelCase (					lowercase_ ,  lowercase_ ,  unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_											=					(
      (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
  )
  lowerCAmelCase_											=					() if is_tf_available() else ()
  lowerCAmelCase_											=					(
      {
          '''feature-extraction''': TFTransfoXLModel,
          '''text-classification''': TFTransfoXLForSequenceClassification,
          '''text-generation''': TFTransfoXLLMHeadModel,
          '''zero-shot''': TFTransfoXLForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
  lowerCAmelCase_											=					False
  lowerCAmelCase_											=					False
  lowerCAmelCase_											=					False
  lowerCAmelCase_											=					False


  def 	UpperCamelCase__   (					self     :       Optional[int]				,						UpperCamelCase__     :       Tuple				,						UpperCamelCase__     :       str				,						UpperCamelCase__     :       Any				,						UpperCamelCase__     :       Optional[Any]				,						UpperCamelCase__     :       List[Any]	)  ->    Union[str, Any]:


    if pipeline_test_casse_name == "TextGenerationPipelineTests":
      # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
      # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
      # tokenizer.
      return True

    return False


  def 	UpperCamelCase__   (					self     :       Dict	)  ->    Optional[Any]:
    _UpperCamelCase							      =TFTransfoXLModelTester(self	)
    _UpperCamelCase							      =ConfigTester(self				,						config_class=UpperCamelCase__				,						d_embed=37	)


  def 	UpperCamelCase__   (					self     :       int	)  ->    Any:
    self.config_tester.run_common_tests()


  def 	UpperCamelCase__   (					self     :       Tuple	)  ->    Tuple:
    self.model_tester.set_seed()
    _UpperCamelCase							      =self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_transfo_xl_model(*UpperCamelCase__	)


  def 	UpperCamelCase__   (					self     :       Optional[Any]	)  ->    Union[str, Any]:
    self.model_tester.set_seed()
    _UpperCamelCase							      =self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCamelCase__	)


  def 	UpperCamelCase__   (					self     :       Optional[Any]	)  ->    Any:
    _UpperCamelCase							      =self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCamelCase__	)


  def 	UpperCamelCase__   (					self     :       List[str]	)  ->    Union[str, Any]:
    _UpperCamelCase      ,  _UpperCamelCase							      =self.model_tester.prepare_config_and_inputs_for_common()
    _UpperCamelCase							      =[TFTransfoXLForSequenceClassification]

    for model_class in self.all_model_classes:
      _UpperCamelCase							      =model_class(UpperCamelCase__	)
      assert isinstance(model.get_input_embeddings()				,						tf.keras.layers.Layer	)
      if model_class in list_other_models_with_output_ebd:
        _UpperCamelCase							      =model.get_output_embeddings()
        assert isinstance(UpperCamelCase__				,						tf.keras.layers.Layer	)
        _UpperCamelCase							      =model.get_bias()
        assert name is None
      else:
        _UpperCamelCase							      =model.get_output_embeddings()
        assert x is None
        _UpperCamelCase							      =model.get_bias()
        assert name is None


  def 	UpperCamelCase__   (					self     :       Dict	)  ->    List[Any]:
    pass


  @slow
  def 	UpperCamelCase__   (					self     :       List[str]	)  ->    List[Any]:


    for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      _UpperCamelCase							      =TFTransfoXLModel.from_pretrained(UpperCamelCase__	)
      self.assertIsNotNone(UpperCamelCase__	)




  @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''	)
  def 	UpperCamelCase__   (					self     :       Any	)  ->    Optional[Any]:
    pass







@require_tf
class 		UpperCAmelCase (					unittest.TestCase):


  """simple docstring"""







  @unittest.skip('''Skip test until #12651 is resolved.'''	)
  @slow
  def 	UpperCamelCase__   (					self     :       List[Any]	)  ->    Union[str, Any]:
    _UpperCamelCase							      =TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''	)
    # fmt: off
    _UpperCamelCase							      =tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]]				,						dtype=tf.intaa	)  # noqa: E231
    # fmt: on
    #  In 1991 , the remains of Russian Tsar Nicholas II and his family
    #  ( except for Alexei and Maria ) are discovered .
    #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
    #  remainder of the story . 1883 Western Siberia ,
    #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
    #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
    #  father initially slaps him for making such an accusation , Rasputin watches as the
    #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
    #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
    #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

    # fmt: off
    _UpperCamelCase							      =[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # noqa: E231
    # fmt: on
    #  In 1991, the remains of Russian Tsar Nicholas II and his family (
    #  except for Alexei and Maria ) are discovered. The voice of young son,
    #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
    #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
    #  and a group of men to perform magic. Rasputin has a vision and
    #  denounces one of the men as a horse thief. Although his father initially
    #  slaps him for making such an accusation, Rasputin watches as the man
    #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
    #  of the Virgin Mary, prompting him to become a priest.
    #  Rasputin quickly becomes famous, with people, even a bishop, begging for
    #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
    # Nicholas II and his family were discovered. The voice of <unk> young son,
    # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

    _UpperCamelCase							      =model.generate(UpperCamelCase__				,						max_length=200				,						do_sample=UpperCamelCase__	)
    self.assertListEqual(output_ids[0].numpy().tolist()				,						UpperCamelCase__	)












'''simple docstring'''







import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
lowerCAmelCase_     						=				'''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase_     						=				direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
lowerCAmelCase_     						=				re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowerCAmelCase_     						=				re.compile(R'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCAmelCase_     						=				re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCAmelCase_     						=				[
    ('''pretraining''', '''MODEL_FOR_PRETRAINING_MAPPING_NAMES''', '''AutoModelForPreTraining'''),
    ('''feature-extraction''', '''MODEL_MAPPING_NAMES''', '''AutoModel'''),
    ('''audio-classification''', '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForAudioClassification'''),
    ('''text-generation''', '''MODEL_FOR_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForCausalLM'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_CTC_MAPPING_NAMES''', '''AutoModelForCTC'''),
    ('''image-classification''', '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForImageClassification'''),
    ('''image-segmentation''', '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES''', '''AutoModelForImageSegmentation'''),
    ('''fill-mask''', '''MODEL_FOR_MASKED_LM_MAPPING_NAMES''', '''AutoModelForMaskedLM'''),
    ('''object-detection''', '''MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES''', '''AutoModelForObjectDetection'''),
    (
        '''zero-shot-object-detection''',
        '''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES''',
        '''AutoModelForZeroShotObjectDetection''',
    ),
    ('''question-answering''', '''MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES''', '''AutoModelForQuestionAnswering'''),
    ('''text2text-generation''', '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForSeq2SeqLM'''),
    ('''text-classification''', '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForSequenceClassification'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES''', '''AutoModelForSpeechSeq2Seq'''),
    (
        '''table-question-answering''',
        '''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForTableQuestionAnswering''',
    ),
    ('''token-classification''', '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForTokenClassification'''),
    ('''multiple-choice''', '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES''', '''AutoModelForMultipleChoice'''),
    (
        '''next-sentence-prediction''',
        '''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES''',
        '''AutoModelForNextSentencePrediction''',
    ),
    (
        '''audio-frame-classification''',
        '''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForAudioFrameClassification''',
    ),
    ('''audio-xvector''', '''MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES''', '''AutoModelForAudioXVector'''),
    (
        '''document-question-answering''',
        '''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForDocumentQuestionAnswering''',
    ),
    (
        '''visual-question-answering''',
        '''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForVisualQuestionAnswering''',
    ),
    ('''image-to-text''', '''MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES''', '''AutoModelForVision2Seq'''),
    (
        '''zero-shot-image-classification''',
        '''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForZeroShotImageClassification''',
    ),
    ('''depth-estimation''', '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES''', '''AutoModelForDepthEstimation'''),
    ('''video-classification''', '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForVideoClassification'''),
    ('''mask-generation''', '''MODEL_FOR_MASK_GENERATION_MAPPING_NAMES''', '''AutoModelForMaskGeneration'''),
]







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'      ,UpperCAmelCase						)
   return [m.group(0						) for m in matches]







def 						_A      ( ):






   '''simple docstring'''







   A__       =     transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
   A__       =     {
       config.replace('Config'      ,''						): model_type for model_type, config in config_maping_names.items()
   }

   # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
   A__       =     collections.defaultdict(UpperCAmelCase						)
   A__       =     collections.defaultdict(UpperCAmelCase						)
   A__       =     collections.defaultdict(UpperCAmelCase						)

   # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
   for attr_name in dir(UpperCAmelCase						):
      A__       =     None
      if _re_tf_models.match(UpperCAmelCase						) is not None:
         A__       =     tf_models
         A__       =     _re_tf_models.match(UpperCAmelCase						).groups()[0]
      elif _re_flax_models.match(UpperCAmelCase						) is not None:
         A__       =     flax_models
         A__       =     _re_flax_models.match(UpperCAmelCase						).groups()[0]
      elif _re_pt_models.match(UpperCAmelCase						) is not None:
         A__       =     pt_models
         A__       =     _re_pt_models.match(UpperCAmelCase						).groups()[0]

      if lookup_dict is not None:
         while len(UpperCAmelCase						) > 0:
            if attr_name in model_prefix_to_model_type:
               A__       =     True
               break
            # Try again after removing the last word in the name
            A__       =     ''.join(camel_case_split(UpperCAmelCase						)[:-1]						)

   A__       =     set(list(pt_models.keys()						) + list(tf_models.keys()						) + list(flax_models.keys()						)						)
   A__       =     list(UpperCAmelCase						)
   all_models.sort()

   A__       =     {'model_type': all_models}
   A__       =     [pt_models[t] for t in all_models]
   A__       =     [tf_models[t] for t in all_models]
   A__       =     [flax_models[t] for t in all_models]

   # Now let's use the auto-mapping names to make sure
   A__       =     {}
   for t in all_models:
      if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
         A__       =     'AutoProcessor'
      elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
         A__       =     'AutoTokenizer'
      elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
         A__       =     'AutoFeatureExtractor'
      else:
         # Default to AutoTokenizer if a model has nothing, for backward compatibility.
         A__       =     'AutoTokenizer'

   A__       =     [processors[t] for t in all_models]

   return pd.DataFrame(UpperCAmelCase						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     [
       transformers_module.models.auto.modeling_auto,
       transformers_module.models.auto.modeling_tf_auto,
       transformers_module.models.auto.modeling_flax_auto,
   ]
   for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
      A__       =     [model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
      A__       =     [auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
      # Loop through all three frameworks
      for module, cls, mapping in zip(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):
         # The type of pipeline may not exist in this framework
         if not hasattr(UpperCAmelCase      ,UpperCAmelCase						):
            continue
         # First extract all model_names
         A__       =     []
         for name in getattr(UpperCAmelCase      ,UpperCAmelCase						).values():
            if isinstance(UpperCAmelCase      ,UpperCAmelCase						):
               model_names.append(UpperCAmelCase						)
            else:
               model_names.extend(list(UpperCAmelCase						)						)

            # Add pipeline tag and auto model class for those models
         table.update({model_name: (pipeline_tag, cls) for model_name in model_names}						)

   return table







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     get_frameworks_table()
   A__       =     Dataset.from_pandas(UpperCAmelCase						)

   A__       =     hf_hub_download(
       'huggingface/transformers-metadata'      ,'pipeline_tags.json'      ,repo_type='dataset'      ,token=UpperCAmelCase						)
   A__       =     Dataset.from_json(UpperCAmelCase						)
   A__       =     {
       tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
       for i in range(len(UpperCAmelCase						)						)
   }
   A__       =     update_pipeline_and_auto_class_table(UpperCAmelCase						)

   # Sort the model classes to avoid some nondeterministic updates to create false update commits.
   A__       =     sorted(table.keys()						)
   A__       =     pd.DataFrame(
       {
           'model_class': model_classes,
           'pipeline_tag': [table[m][0] for m in model_classes],
           'auto_class': [table[m][1] for m in model_classes],
       }						)
   A__       =     Dataset.from_pandas(UpperCAmelCase						)

   with tempfile.TemporaryDirectory() as tmp_dir:
      frameworks_dataset.to_json(os.path.join(UpperCAmelCase      ,'frameworks.json'						)						)
      tags_dataset.to_json(os.path.join(UpperCAmelCase      ,'pipeline_tags.json'						)						)

      if commit_sha is not None:
         A__       =     (
             F"""Update with commit {commit_sha}\n\nSee: """
             F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
         )
      else:
         A__       =     'Update'

      upload_folder(
          repo_id='huggingface/transformers-metadata'      ,folder_path=UpperCAmelCase      ,repo_type='dataset'      ,token=UpperCAmelCase      ,commit_message=UpperCAmelCase      ,)







def 						_A      ( ):






   '''simple docstring'''







   A__       =     {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
   A__       =     transformers_module.pipelines.SUPPORTED_TASKS
   A__       =     []
   for key in pipeline_tasks:
      if key not in in_table:
         A__       =     pipeline_tasks[key]['pt']
         if isinstance(UpperCAmelCase      ,(list, tuple)						):
            A__       =     model[0]
         A__       =     model.__name__
         if model not in in_table.values():
            missing.append(UpperCAmelCase						)

   if len(UpperCAmelCase						) > 0:
      A__       =     ', '.join(UpperCAmelCase						)
      raise ValueError(
          'The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '
          F"""`utils/update_metadata.py`: {msg}. Please add them!"""						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    parser.add_argument('''--token''', type=str, help='''The token to use to push to the transformers-metadata dataset.''')
    parser.add_argument('''--commit_sha''', type=str, help='''The sha of the commit going with this update.''')
    parser.add_argument('''--check-only''', action='''store_true''', help='''Activate to just check all pipelines are present.''')
    lowerCAmelCase_     						=				parser.parse_args()

    if args.check_only:
        check_pipeline_tags()
    else:
        update_metadata(args.token, args.commit_sha)


